class_docstr: Base class for hooks based on summary metrics.
  
  Subclasses should override _process_metrics.
  
  If _process_metrics returns True, calls run_context.request_stop().
  
  This can be used to something like "Stop after the loss has stopped decreasing
  for 5000 steps.
code: def _init_multiplexer(self):
      dirs = [
          os.path.join(self._events_dir, subdir)
          for subdir in self._subdirs
      ]
      run_path_map = dict(
          [(self._RUN_NAME % i, d) for i, d in enumerate(dirs)]
      )
      return event_multiplexer.EventMultiplexer(run_path_map)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tensorflow.compat.v1
  from tensorboard.backend.event_processing import event_accumulator
  from tensorboard.backend.event_processing import event_multiplexer
label_desc: Use image node_id 2 for calling the MetricsBasedHook obj's underlying member method code with example usage: obj._init_multiplexer() and returns: event_multiplexer